Huang and Jianxi Huang designed the research, performed the analysis, and wrote the paper; Xuecao Li, Wen Zhuo, Yantong Wu, Quandi Niu, Wei Su, and Wenping Yuan edited and revised the manuscript. Code availability Python scripts that implement model calibration, data assimilation, dataset generation, and mapping are available ( https://github.com/paperoses/CHN_Winter_Wheat_AGB ). Further questions can be directed towards Hai Huang (haihuang@cau.edu.cn). Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information The on
Open resource ↗paperoses/CHN_Winter_Wheat_AGB · lines:117-143Unverified paper record
A dataset of winter wheat aboveground biomass in China during 2007-2015 based on data assimilation.
Scientific data · 11 May 2022 · 10.1038/s41597-022-01305-6
Abstract
As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R 2 is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha -1 . Compared to county-level statistics during 2007-2015, the ranges of R 2 , RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha -1 , and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.
Plant phenotyping relevance
冬小麦の地上部バイオマスという明示的な植物形質を、作物モデルとMODIS LAIデータ同化で広域推定し、地上測定および統計値で検証したデータセット研究であり、形質推定手法と検証が中心です。
abstractwe assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015.
abstractThe validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations
Code and data availability
The paper's winter wheat AGB analysis code is publicly available on the authors' GitHub repository, and the MODIS LAI product assimilated into WOFOST is publicly accessible via the Land-Atmosphere Interaction Research Group website. The generated AGB dataset itself is on figshare (10.6084/m9.figshare.16680784.v3), but那
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